System and method for antenna beam selection

ABSTRACT

We generally describe a system (100) for antenna beam selection in a radio access network. The system (100) comprises a measurement module (102) which is configured to obtain channel quality measurements of a plurality of beams usable to serve a user equipment (112) in the 5G radio access network. The system (100) further comprises a training module (104) coupled to the measurement module (102), wherein the training module (104) is configured to generate a machine learning model based on the channel quality measurements. The system (100) further comprises a prediction module (106) coupled to the training module (104), wherein the prediction module (106) is configured to receive the machine learning model from the training module (104), and select, based on the machine learning model, one of the plurality of beams used to serve the user equipment (112).

TECHNICAL FIELD

The present disclosure generally relates to systems and methods forantenna beam selection in 5G radio access networks (RANs) based onmachine learning, in particular using distributed cloud-based machinelearning.

BACKGROUND

Machine Learning

Machine learning (ML) techniques have recently gained a lot ofattention. It enables software components to learn from experiencesinstead of being explicitly programmed for a specific task. One of themain focuses of machine learning research is to extract information fromdata automatically using computational and statistical methods. Thebusiness potential and some possible applications of machine learning inthe telecommunication domain have been discussed in the art. Somehigh-level areas have been described where machine learning can createbusiness value in the telecommunication domain including personalization(user segmentation, user profiling), recommendation (hints about userpreferences) and media recognition (e.g., automated tagging andcategorization of media content). Applications of machine learning forenhancing performance of 4G radio access network functions have alsobeen in the focus of research.

Beam Selection in 5G

Advanced antennas are a crucial part of, for example, 5G radio access.These antennas will typically be used at high carrier frequencies inorder to achieve high throughput. At high frequencies, the propagationbecomes more hostile. However, as the carrier frequency gets higher, theantenna elements get smaller. This enables packing more antenna elementsinto a smaller antenna. For example, at 15 GHz, it is possible to designan antenna with 200 elements that is only 5 cm wide and 20 cm tall. Withmore antenna elements, it becomes possible to produce antenna systemswhere many directed antenna beams are created to cover a relativelynarrow geographical area. Using these advanced antenna techniques, thenetwork can select a serving beam that may have the best coverage forthe intended receiver. Since these beams are quite narrow in terms ofbeam-width, and thus the transmission is concentrated into a certaindirection, the signal strength can be significantly improved, whileinterference can be reduced due to the relatively lower number of serveduser equipment (UEs) per beam.

The beam selection problem in the 5G system is concerned with how toselect a beam to serve the user equipment dynamically which has thehighest channel quality in order to provide the best coverage andminimize interference.

Existing beam selection solutions typically rely on measuring a largenumber of reference signals from each of the antennas and determiningthe optimal beam which is selected to serve the user equipment based onthese measurements. The main drawback of these solutions is that theyrequire constantly measuring a large number of reference signals, whichconsumes a considerable amount of radio capacity and requiressignificant terminal time and battery resources. As a result, systemcapacity may be reduced.

Measuring only a subset of the signals and using prediction andinterpolation techniques to estimate the rest of the reference signalsare one way to overcome these drawbacks. Machine learning is onepossible technique to perform such predictions. However, most of theexisting machine learning solutions are applied in the fields of imageprocessing, computer vision and voice recognition.

Some systems use machine learning algorithms to re-shape the antenna'sradiation pattern such that a closed form objective function ismaximized, as shown, e.g., in U.S. Pat. No. 8,942,659 B2. However, nopractical aspects, such as limited processing and measurementcapabilities as well as tight delay constraints, have been mentioned.These aspects may be important since, in practice, the beam selectionmay be executed on a millisecond (msec) level depending on the speed ofthe user equipment. The optimal beam selection may require a lot ofchannel state information (CSI) and some computationally heavy algorithm(e.g., an exhaustive search) which may limit practical applications ofsuch solutions. Neural networks have been used to change betweendifferent antenna configurations for reconfigurable antennas. Thissolution is tailored to a specific type of antenna which the neuralnetwork is trained for. Furthermore, it uses special hardware(Field-Programmable Gate Arrays, FPGAs) to run the implementation.

SUMMARY

It has been realized that no solution has been thought of thus far wherea practically applicable algorithm is taken into consideration in thebeam selection problem which can be directly applied in the fast controlloop and requires only a fraction of CSI measurements from the terminal.

Moreover, it has been realized that there are no solutions available forhow to execute machine learning tasks in a scalable and feasible mannerutilizing the available compute and communication resources not only butalso in, for example, a 5G network environment, such that the stringentdelay and reliability requirements of the beam selection procedure isfulfilled without the need to place additional hardware resources ontothe (5G) radio boards. It has been realized that there is a conflictingrequirement, which on one hand would mandate deploying all of thecompute onto the radio board to fulfil delay requirements, while at thesame time there is a tendency to move compute and functionality(including some of the baseband functions) further away, for exampleinto the cloud.

According to a first aspect of the present disclosure, there is provideda system for antenna beam selection in a radio access network, thesystem comprising: a measurement module configured to obtain channelquality measurements of a plurality of beams usable to serve a userequipment in the radio access network; a training module coupled to themeasurement module, wherein the training module is configured togenerate a machine learning model based on the channel qualitymeasurements; and a prediction module coupled to the training module,wherein the prediction module is configured to: receive the machinelearning model from the training module, and select, based on themachine learning model, one of the plurality of beams used to serve theuser equipment.

The system therefore provides for a near-optimal selection algorithmwhich uses machine learning techniques for selecting the best beam whichis to be used to serve the user equipment.

The measurement module may, in some examples, be configured to conductthe channel quality measurements itself. In some other examples, themeasurement module may be configured to collect the channel qualitymeasurements which themselves are conducted by another unit which may ormay not be integral to the system for antenna beam selection asdescribed herein.

In one variant of the system, the training module is located in a cloudenvironment. The algorithm may hereby be split into two parts in orderfor it to be deployed feasibly given existing hardware and softwareresources in a system, such as, but not limited to a 5G system, takinginto account the limited resources on the radio hardware boards and theavailability of more compute resources in the cloud, such as, but notlimited to a 5G cloud.

In some embodiments, the prediction module may be comprised in a radiounit, such as, for example, a 5G radio unit. It is to be noted that thesystem will not only be applicable for a 5G radio unit. Therefore, anyreference in the present disclosure to a 5G radio unit, 5G basebandprocessing unit or other 5G-related implementations are equallyapplicable in other generation systems.

In some embodiments, the measurement module is configured to obtain andprovide to the training module, during a training phase, all channelquality measurements of the plurality of beams, wherein the trainingmodule is configured to update the machine learning model based on allchannel quality measurements of the plurality of beams received from themeasurement module. The machine learning model may hereby be updated,for example, continuously during the training phase.

In some variants of the system, the prediction module is furtherconfigured to: continuously determine whether a new machine learningmodel is received from the training module, and when the channel qualitymeasurements are obtained by the measurement module (which themeasurement module may, in some examples, receive from the userequipment): send a request to the measurement module to provide, to theprediction module, channel quality of a subset of the plurality ofbeams, predict the selected beam based on the channel quality of thesubset of the plurality of beams by feeding the channel qualitymeasurements into the machine learning model, and output the selectedbeam to cause a baseband processing unit (BPU) (such as, but not limitedto a 5G BPU) to switch its connection with the user equipment to theselected beam.

The prediction module may hereby request reference beam measurementscorresponding to the subset of the plurality of beams which may beneeded as model input, whereby the prediction module then provides thepredicted best beam as the output. Based on the prediction, the basebandprocessing unit may then switch (handover) the connection according tothe received target beam ID.

In some variants, the prediction module may further be configured toperform the sending of the request to the measurement module to provide,to the prediction module, channel quality of the subset of the pluralityof beams, the predicting of the selected beam based on the channelquality of the subset of the plurality of beams and the outputting ofthe selected beam to cause the baseband processing unit to switch itsconnection with the user equipment to the selected beam until a timeperiod, starting from the new machine learning model being received bythe prediction module, exceeds a predetermined threshold time period.Information regarding the predicted best beam may then be updatedcontinuously and sent to the baseband processing unit, which may be a 5Gbaseband processing unit.

In some variants, the prediction module is further configured to:periodically determine accuracy of the machine learning model receivedfrom the training module, and if the accuracy is larger than a thresholdaccuracy, send a measurement inactivation command to the measurementmodule to inactivate channel quality measurements to be obtained ofbeams not comprised in the subset of the plurality of beams, and if theaccuracy is smaller than the threshold accuracy, send an accuracywarning report to the training module to cause the training module torefine the machine learning model. This procedure may be important asthe accuracy of the model may degrade due to, for example, a changinglayout (for example a new building has been built), a newly set upnetwork, weather or seasonal reasons/changes, or other conditionalchanges.

In a further variant, the prediction module is further configured toperform the determination of accuracy of the machine learning model by:measuring the channel quality of each of the plurality of beams, andcomparing the outcome of the measurement of the channel quality of eachof the plurality of beams with the machine learning model received fromthe training module. The direct comparison between the measurements ofthe channel quality of each of the plurality of beams with the output ofthe machine learning model may thereby allow for further refinement ofthe machine learning model by the training module.

In a further variant of the system, the prediction module is configuredto perform the determination of accuracy of the machine learning modeland the selection of the beam used to serve the user equipment onseparate threads. Interference between the accuracy check of the machinelearning model and the periodic prediction task main thread may herebybe avoided.

In some variants, the system is configured to select the subset from theplurality of beams such that the beams of the subset cover asubstantially uniform area of the plurality of beams. This may allow,for example, for improving the accuracy check of the machine learningmodel.

In some further variants of the system, during a prediction phase, theprediction of the selected beam by the prediction module is based onlyon the channel quality of the subset of the plurality of beams. In someexamples, in which the measurement module is configured to obtain andprovide to the training module, during the training phase, all channelquality measurements of the plurality of beams, and wherein the trainingmodule is configured to update the machine learning model based on allchannel quality measurements of the plurality of beams received from themeasurement module, the system may be configured to switch between thetraining phase and the prediction phase. This may allow for the systemto generate from a multiple switching between the training phase and theprediction phase a heuristic model usable to predict the beam with thehighest channel quality among the plurality of beams.

In some further variants of the system, the training module isconfigured to generate the machine learning model based on channelquality measurements associated with a plurality of user equipment inthe radio access network, which may be a 5G radio access network. Themachine learning model may hereby, for example, be generated in ashorter amount of time.

In a further variant, the system further comprises a monitoring moduleconfigured to continuously monitor performance metrics obtained via oneor both of the prediction module and the training module. The monitoringmodule may hereby be used to continuously monitor the proposed algorithmin order to calculate and visualize some important performance metrics,such as, but not limited to the performance reports from the predictionmodule sent after each prediction which may contain information on theaccuracy of the actual prediction, the delay of the prediction, andother performance metrics.

In some variants, the training module is further configured to generatethe machine learning model based on channel quality measurementsobtained from a portion of the plurality of beams. This may allow foraggregation of the channel quality measurements. For example, themachine learning model may be calculated for those beam IDs that aremost often seen as the dominant beam IDs by the reporting userequipment, or, in some instances, one model per beam may be calculated.

In a further variant of the system, the training module is furtherconfigured to determine whether a machine learning model is available inits database which was generated earlier, and if no machine learningmodel is available in its database, the training module is configured toactivate, by sending an activation request to the measurement module,the channel quality measurements to be obtained by the measurementmodule of the plurality of beams if the channel quality measurementshave not yet been activated. This may allow for making use of a machinelearning model which has been generated earlier in order to start theprediction process. Furthermore, channel quality measurements may onlybe performed, in some scenarios, in order to start the machine learningmodel generation process and the prediction process if no machinelearning model has been generated earlier and stored in the trainingmodule database, thereby reducing the required channel qualitymeasurements performed by the terminal.

In some variants of the system, the training module is furtherconfigured to control the number of channel quality measurements to beobtained by the measurement module for the training module to be able togenerate the machine learning model. This may allow for only so manychannel quality measurements to be performed which are needed for thetraining module to be able to generate the machine learning model.

In further variants of the system, the training module is furtherconfigured to: determine accuracy of the machine learning model, and ifaccuracy of the machine learning model is below a predeterminedthreshold, collect further channel quality measurements for generatingthe machine learning model, and if accuracy of the machine learningmodel is above the predetermined threshold, send the machine learningmodel to the prediction module. The training module may hereby becoupled to the measurement module such that the training module candirectly control the measurement module to obtain further channelquality measurements if accuracy of the machine learning model is belowthe predetermined threshold. Furthermore, accuracy of the prediction bythe prediction module may be enhanced or at least maintained as a (insome cases an updated) machine learning model is only sent from thetraining module to the prediction module if accuracy of the machinelearning model is above the predetermined threshold. It is to be notedthat the predetermined threshold of the machine learning model mayhereby be identical to the threshold accuracy described above.

In a further variant of the system, the training module is furtherconfigured to send a measurement inactivation command to the measurementmodule to inactivate channel quality measurements to be obtained ifaccuracy of the machine learning model is above the predeterminedthreshold.

In one variant, the system is further configured to create differentmachine learning models for different corresponding, respective cellsites of the radio access network, and select one of the machinelearning models based on a location of the user equipment in one of thecell sites or a set of cells to predict the beam with the highestchannel quality among the plurality of beams. One cell may hereby belongto multiple models so that overlaps between the models are providedwhich may result in approved overall prediction accuracy.

In a related aspect of the present disclosure, there is provided amethod for antenna beam selection in a radio access network (which maybe a 5G radio access network), the method comprising: obtaining channelquality measurements of a plurality of beams useable to serve a userequipment in the radio access network; generating a machine learningmodel based on the channel quality measurements; and selecting, based onthe machine learning model, one of the plurality of beams to serve theuser equipment.

Variants of the method for antenna beam selection in a radio accessnetwork correspond to those variants as described above with regard tothe system for antenna beam selection in a radio access network. Inparticular, generating the machine learning model based on the channelquality measurements may be performed in a cloud environment. Predictingthe best (i.e. highest channel quality) beam which is selected, based onthe machine learning model, to serve the user equipment may be performedin a radio unit (which may be a 5G radio unit).

In a further related aspect of the present disclosure, there is provideda computer program product comprising program code portions forperforming the method or variants of the method as described above whenthe computer program product is executed on one or more computingdevices. The computer program product may hereby be stored on acomputer-readable recording medium.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects of the present disclosure will now be furtherdescribed, by way of example only, with reference to the accompanyingfigures, wherein like reference numerals refer to like parts, and inwhich:

FIG. 1 shows a schematic block diagram of a cloud-based antenna beamselection system according to variants of the present disclosure;

FIG. 2 shows a flowchart of a machine learning model training algorithmaccording to variants of the present disclosure;

FIG. 3 shows a flowchart of a prediction algorithm according to variantsof the present disclosure;

FIGS. 4a and b show a sequence chart of a beam selection algorithmaccording to variants of the present disclosure;

FIG. 5 shows a graphical user interface relating to a beam selectionalgorithm according to variants of the present disclosure;

FIG. 6 shows a delay monitoring chart according to variants of thepresent disclosure;

FIG. 7 shows accuracy monitoring charts according to variants of thepresent disclosure;

FIG. 8 shows a schematic block-diagram of an antenna beam selectionapparatus according to variants of the present disclosure;

FIG. 9 shows a further schematic block-diagram of a 5G radio unit and acloud environment according to variants of the present disclosure;

FIG. 10 shows a flowchart of a method for antenna beam selection in a 5Gradio access network according to variants of the present disclosure;and

FIG. 11 shows a schematic diagram of model coverage areas according tovariants of the present disclosure.

DETAILED DESCRIPTION

The near-optimal beam selection algorithm presented herein uses machinelearning techniques to significantly reduce the number of channelquality measurements required to select the best beam for the userequipment. The algorithm is, in some examples, split into two parts inorder for it to be deployed feasibly given the existing hardware andsoftware resources in a 5G system, taking into account the limitedresources on the radio hardware boards and the availability of morecompute resources in the 5G cloud.

FIG. 1 shows a schematic block diagram of a cloud-based antenna beamselection system 100 according to variants of the system as describedherein.

As illustrated in FIG. 1, the algorithm runs in a distributed way,meaning that the machine learning model training part can be deployed inthe 5G cloud where computing resources (Central Processing Unit (CPU)and memory) are typically not limited, while the model execution can bedeployed on the 5G radio board, which enables basically zero propagationdelay between the prediction and execution of the predicted action inthe 5G radio unit. Thereby, the algorithm can achieve millisecond-levelbeam selection which makes it possible to be applied in the fast controlloop of the beam selection algorithm, without requiring additionalcompute resources on the 5G radio hardware.

In this example, the system 100 comprises a measurement module 102(denoted “measurement collector” in FIG. 1) which is configured toobtain channel quality measurements of a plurality of beams usable toserve a user equipment 112 in the 5G radio access network. The channelquality measurements are provided to the measurement module 102 by the5G baseband processing unit 108, which is, in this example, configuredas a remote radio unit. At the same time, the measurement module 102 isconfigured to control the measurement configuration of the 5G basebandprocessing unit 108.

The system 100 further comprises a training module 104 (denoted “MLmodel training” in FIG. 1) which is coupled to the measurement module102. The training module 104 is configured to receive channel qualityinformation on all beams from the measurement module 102. This allowsthe training module 104 to generate a machine learning model based onthe channel quality measurements. The training module 104 is, in thisexample, further configured to trigger (in)activation of measurements onnon-reference beams, as will be further described in more detail below.

The system 100 further comprises a prediction module 106 (denoted“Prediction” in FIG. 1) which is coupled, in this example, to each ofthe training module 104 and the measurement module 102. The coupling ofthe prediction module 106 to the training module 104 allows for theprediction module 106 to receive a machine learning model update fromthe training module 104. At the same time, the prediction module 106 is,in this example, configured to send an accuracy warning report to thetraining module 104 in case accuracy of the machine learning model dropsor is below a certain predetermined threshold.

In this example, the system 100 further comprises a monitoring module110 (denoted “Monitoring processes” in FIG. 1), which is generallyoptional, coupled to each of the training module 104 and the predictionmodule 106. In this example, the training module 104 provides themonitoring module 110 with performance reports regarding, for example,delay of training and other performance metrics. The prediction module106 provides the monitoring module 110, in this example, withperformance reports regarding, for example, accuracy and delay ofprediction and other performance metrics.

In this example, the prediction module 106 is coupled to the measurementmodule 102, whereby the prediction module 106 is configured to trigger(in)activation of measurements obtained by the measurement module 102.At the same time, the measurement module 102 is configured to providechannel quality on reference beams to the prediction module 106, as willbe further described in detail below.

Based on the machine learning model received by the prediction module106 from the training module 104, the prediction module 106 can selectone of the plurality of beams which is then used to serve the userequipment 112. In this example, the information of the hereby predictedbest (highest-quality) beam is provided by the prediction module 106 tothe 5G baseband processing unit 108.

The 5G baseband processing unit 108 may then switch, if necessary, itscommunication channel with the user equipment 112 to the highest-qualitybeam as predicted by the prediction module 106.

In this example, the 5G baseband processing unit 108, the measurementmodule 102 and the prediction module 106 are comprised in a 5G radiounit 114.

Furthermore, in this example, the training module 104 and the monitoringmodule 110 are comprised in a local cloud (cloud environment 116).

As can be seen from the above, FIG. 1 illustrates the block diagram ofthe beam selection system 100 which consists of the three maincomponents of the measurement module 102, the training module 104 andthe prediction module 106.

In the case of beam selection, what may need to be learned is therelationship of the beams through channel quality measurements. One mayassume that M beams Beam₀, . . . , Beam_(M-1) are available, and out ofthese M beams only a subset is measured, which is the set of referencebeams. In other words, one needs to find a predictor function that, forexample, can return the channel quality on Beam₀, if the channel qualityQ_(R1) on reference beam Beam_(R1) and the channel quality Q_(R2) onreference beam Beam_(R2) are measured. Accuracy of the learned functionlargely depends on the selected input feature representations. Thefeatures may be selected such that they best describe the problem, i.e.,they preferably comprise enough information on the output in order toachieve high accuracy in prediction. In this case, for the inputfeatures one can define a vector as [Q_(R0), Q_(R1), . . . , Q_(RN)],where Q_(RN) denotes the channel quality measured on reference beamBeam_(n). The reference beams may always be switched on and becontinuously measured in terms of channel quality.

The reference beams may be selected arbitrarily. However, in thisexample, the reference beams are selected such that the coverage area ofall beams is uniformly covered by the reference beams in order to samplethe channel quality from the area more or less uniformly. The actualselected reference beams and the number of reference beams may have adirect impact on accuracy of the machine learning model.

The labelled target value is, in this example, the ID of the beam wherechannel quality is measured to be the highest. Since the trainingdataset can be generated, in some instances, together with the labelledoutput, supervised learning algorithms may be applied to theabove-defined problem.

FIG. 2 shows a flowchart 200 of a machine learning model trainingalgorithm according to variants as described herein. In this example,the machine learning model training algorithm runs in a local cloud.

Once the process has started, the training module 104 first checks atstep S202 whether a machine learning model is available in its database(which may be stored in its memory) which was calculated earlier. If amachine learning model is available in its database, the training module104 calculates the model accuracy at step S210. From there, the trainingmodule 104 determines as to whether the accuracy is above apredetermined threshold at step S212. If the accuracy is above thepredetermined threshold, the machine learning model is updated at stepS220 in the memory of the training module 104 if it changed. However, ifthe accuracy is below the predetermined threshold as determined at stepS212, the training module 104 collects more measurements at step S208which are used to generate the machine learning model.

If it is determined by the training module 104 at step S202 that nomachine learning model is available in its database, it activates thechannel quality measurements on all beams at step S206 if they aredetermined at step S204 to not yet be activated. The training module 104hereby sends an activation request at step S206 to the measurementgenerator which may be coupled to or comprised in the measurement module102. After this, the training module 104 collects as many measurementsat step S208 as are needed in order for it to be able to calculate (insome instances a first version of) the machine learning model. If it isdetermined at step S204 that the channel quality measurements areactivated, the training module 104 goes straight to step S208 where itcollects channel quality measurements needed to generate the machinelearning model. As shown in FIG. 2, the training module 104 herebyobtains the channel quality measurements at step S214 from themeasurement module 102.

After or while measurements are collected at step S208 by the trainingmodule 104, it determines at step S216 as to whether it has collectedenough measurements in order for it to be able to calculate the machinelearning model. If this is not the case, the training module 104collects further measurements as outlined above regarding step S208until enough measurements have been collected by the training module 104so that it can generate the machine learning model.

Once enough measurements have been collected by the training module 104to calculate the machine learning model, the training module 104calculates the machine learning model per aggregation area at step S218.After that, the training module 104 determines as to whether accuracy ofthe machine learning model is above a predetermined threshold at stepS212, as outlined above. If this is not the case, the training module104 goes back to step S208 to collect more channel quality measurements.If however accuracy is above the threshold, as outlined above, thetraining module 104 updates the machine learning model in its databaseat step S220 if the machine learning model has changed.

The training module 104 then sends the machine learning model to theprediction module 106 at step S222. Furthermore, in this example, thetraining module 104 sends a measurement inactivation command to themeasurement generator at step S224.

In this example, the training module 104 then creates and sends theabove-described performance reports at step S226 to the monitoringmodule 110.

In this example, the training module 104 determines at step S228 whetherit has received an accuracy warning report from the prediction module106. If this is a case, the training module 104 goes back to step S206in order to send a measurement activation command to the measurementgenerator such that more channel quality measurements can be collectedby the training module 104 as described above with regard to step S208.

If however no accuracy warning report is determined at step S228 to havebeen received by the training module 104 from the prediction module 106,the training module 104 determines at step S230 as to whether atermination request is received. If this is the case, the machinelearning model training algorithm process in the local cloud ends.However, if no termination request is received, the training module 104is sent to sleep for a predetermined time period. Once this time periodhas elapsed, the training module 104 goes back to step S228 where itdetermines as to whether an accuracy warning report has been receivedfrom the prediction module 106.

As can be seen, the algorithm splits to collected data (channel qualityon reference beams) into training and testing parts. It builds themachine learning model using the training part and then checks theaccuracy of the trained model using the testing dataset. One machinelearning model is calculated per aggregation area in this example. Oneaggregation area can, for example, be the measurements that belong tothe beams of one 5G radio unit. This case is illustrated in FIG. 1.Other separations of the measurements are also possible, as will befurther described below.

The ML model may be created using various supervised machine learningalgorithms, for example, an ensemble learning algorithm (for example,random forest classifier https://en.wikipedia.org/wiki/Random_forest, orgradient boosting classifierhttps://en.wikipedia.org/wiki/Gradient_boosting) or neural network basedapproaches (for example Multilayer perception (MLP) methodhttps://en.wikipedia.org/wiki/Multilayer_perceptron). Common to theselearning algorithms is that they are deriving the functionalrelationship between a set of input variables (i.e., the set ofreference beams, the [QR0, QR1, . . . , QRN] vector in the present case)and a set of output variables (i.e., the best beam) based on observationof a large set of reference data. The reference data in the case asdescribed herein means the measurement samples of all the beamscollected during the learning, i.e., measurement collection phase. Thedifferent machine learning methods differ in terms of the algorithmicdetails how they calculate this functional relationship between the setof input variables and set of output variables. The present disclosurehereby focuses on how to apply such algorithms in the particular contextof, for example, 5G beamforming, how to realize and implement it in thiscontext.

Then, once the model is learnt from the reference data, i.e., thefunctional relationship between input and output variables has beenestablished, the model can be used for prediction. The learnt functionis applied and the current input variables (i.e., the measured referencebeam vector [QR0, QR1, . . . , QRN]) are substituted into the functionto get the desired output variable (i.e., the ID of the best beam).

In this example, after the machine learning model is calculated with atleast the threshold accuracy, TH_(acc), (for example, TH_(acc) can beset to 95% of a perfect accuracy), the machine learning model is stored,in this example, in a persisted (in-memory) database in case the machinelearning training process is restarted. If there is a model available inthe database with greater than TH_(acc) accuracy, then there is no needto recalculate the model. Then, the model is sent to the predictionmodule 106 running on the 5G radio board (unit) and the machine learningtraining process inactivates the measurements on all beams by sending arequest to the measurement module 102. As described above, after this,it listens for any accuracy warning report from the prediction process.This report informs the machine learning training process that theprediction module 106 noticed accuracy degradation of the latest modelused for prediction. As the training module 104 receives such a report,it immediately tries to build a more accurate model by re-executing thenecessary steps (the ones described above regarding the measurementactivation). The machine learning model training process is running, inthis example, in the local cloud, since it requires a relatively largeamount of compute and memory resources that are typically available inthe cloud environment, but are not available on, for example, a networknode that is designed to have dedicated hardware resources.

FIG. 3 illustrates a flowchart 300 of the prediction algorithm running,in this example, in the 5G radio unit as described herein.

Once the prediction algorithm has started, the prediction module 106determines at step S302 as to whether a machine learning model isreceived from the training module 104. If this is not the case, theprediction module 106 continuously performs this determination at stepS302.

Once a machine learning model is received from the training module 104at the prediction module 106, the prediction module 106 sends a requestfor reference beam measurements to the measurement generator at stepS304. As outlined above, the measurement generator may be coupled to orcomprised in the measurement module 102.

At step S308, the prediction module 106 receives further channel qualitymeasurements. These channel quality measurements on reference beams areprovided to the prediction module 106 by the measurement module 102 atstep S306.

The prediction module 106 then predicts at step S310 the best (i.e.highest-quality) beam using the machine learning model received from thetraining module 104. The prediction module 106 then sends, at step S312,information regarding the best beam to the 5G baseband processing unit108.

The prediction module 106 then determines at step S314 as to whether thetime elapsed since the last accuracy check is larger than apredetermined threshold. If this is not the case, the prediction module106 goes back to step S308 where it receives further channel qualitymeasurements on reference beams.

If however the time elapsed since the last accuracy check is larger thanthe predetermined threshold, the prediction algorithm forks at stepS316. On the one hand, the prediction algorithm continues with step S308at which the prediction module 106 receives further channel qualitymeasurements. On the other hand, the prediction module 106 sends ameasurement activation command at step S318 to the measurementgenerator.

The prediction module 106 then calculates the machine learning modelaccuracy at step S320. The prediction module 106 then determines at stepS322 based on the calculated machine learning model accuracy as towhether the accuracy is above or below a predetermined thresholdaccuracy. If this is not the case, the prediction module 106 sends anaccuracy warning report to the training module 104 at step S324. Ifhowever the accuracy is above the predetermined threshold accuracy, theprediction module 106 sends a measurement inactivation command at stepS326 to the measurement generator. The processes of sending themeasurement inactivation command to the measurement generator at stepS326 and the process of sending an accuracy warning report to thetraining module 104 at step S324 join a further process common to bothscenarios at step S328.

The prediction module 106 then determines at step S330 as to whether atermination request has been received. If this is the case, theprediction algorithm process ends. However, if no termination request isdetermined at step S330 to have been received by the prediction module106, the process returns to step S308 at which further channel qualitymeasurements are received by the prediction module 106.

As can be seen from the above, the prediction process is responsible forexecuting the trained model each time a new channel quality measurementof the reference beams arrives from a user equipment. Since the coveredarea of a beam is relatively small compared to, for example, thetraditional omni or 3-sector antennas, the selection of the bestavailable beam (i.e. the one with the highest quality) preferably runson a much lower timescale (˜5-10 msec) compared to traditional handoveramong cells (˜200-300 msec). As the distributed cloud platform asdescribed herein enables deploying and executing processes remotely, forinstance, on network nodes (i.e., on the 5G radio board), propagationdelay can be minimized compared to a scenario in which prediction issent directly from the cloud to the network nodes.

As outlined above, the prediction process continuously checks whether anew machine learning model has been sent by the training module in thecloud. If the model is received, the prediction module requests thereference beam measurements that are needed as model input and thenprovides the predicted best beam ID as the output. Based on theprediction, the 5G baseband processing unit module switches (handovers)the connection according to the received target beam ID. The predictormodule periodically (every Th_(ch) (threshold check) seconds) checks theaccuracy of the model. In order to do this, the prediction module needsto know the ground truth, i.e., it needs to measure the channel qualityon all beams and then calculate the accuracy of the machine learningmodel by measuring the true best beam (i.e. the beam with the highestquality) and comparing it to the output of the training module.

There may also be alternative ways to measure the accuracy of the modelwithout requiring constant measurement on all the possible beams. Forexample, degraded signal strength after the beam switch or lost radioconnection after the switch can all be an indication of degraded modelaccuracy. When the ratio of such incidents increases above a threshold,the system could trigger an update of the model, first by initiatingmeasurement collection from all the beams and then calculating the model(according to any of the selected machine learning algorithm).

If the accuracy is high enough, it inactivates the unnecessary beammeasurements (on non-reference beams), as outlined above. Otherwise, theprediction module creates and sends an accuracy warning report to thetraining module to refine the current model. Accuracy of the model candegrade due to, for example, a changed layout (a newly built building),a new network setup, weather or seasonal reasons/changes, and otherconditional changes. The model accuracy check and the correspondingcomputing tasks are, in this example, executed on a separate thread inthe implementation compared to the main prediction thread in order notto interfere with the periodic main prediction task thread.

The measurement collector/measurement module 102 is responsible forconfiguring the necessary radio quality measurements in the 5G basebandprocessing unit software according to the requests sent by theprediction module 106 and the training module 104. It is designed as aseparate module, such that the configuration interface of the radio unitneeds to be implemented only once.

The monitoring module 110 is optional, as outlined above, but it may beuseful to continuously monitor the proposed algorithm to calculate andvisualize some important performance metrics. For instance, theperformance reports from the prediction module 106 after each predictionmay contain information on the accuracy of the actual prediction, thedelay of the prediction, etc.

FIGS. 4a and b illustrate the proposed algorithm in a form of a messagesequence diagram 400.

As can be seen, in this example, the training module first activatesmeasurements on all beams to be obtained by the measurement collector.The measurement collector then provides a measurement configuration tothe 5G radio unit. In this example, the 5G radio unit then provides allchannel quality measurements to the measurement collector, which in turnsends all channel quality measurements to the training module.

The training module then collects the channel quality measurements.Further channel quality measurements are provided from the 5G radio unitto the measurement collector, which are again in turn sent by themeasurement collector to the training module. The training module isthen able to create the machine learning model. Furthermore, accuracy ofthe machine learning model is checked.

In this example, the training module then sends the machine learningmodel update to the prediction module. Furthermore, the training modulesends an inactivation measurement command on non-reference beams to themeasurement collector. The measurement collector then sends ameasurement configuration to the 5G radio unit.

The training module provides a performance report to the monitoringunit, which is an optional process.

Continuing with the same process, as can be seen in FIG. 4b , in thisexample, the prediction module then loads the machine learning model. Itthen sends a request for measurements on reference beams to themeasurement collector. The measurement collector then provides thechannel quality measurements on reference beams to the predictionmodule. Based on the above steps, the prediction module then determinesthe best beam, the ID of which is sent by the prediction module to the5G radio unit. Furthermore, the prediction module provides a performancereport to the monitoring unit (which is optional in this process).

In this example, the measurement collector provides further channelquality measurements on reference beams to the prediction module. Basedon the further channel quality measurements on reference beams, theprediction module determines once again the best beam, the ID of whichthe prediction module sends to the 5G radio unit. A performance reportis then sent again from the prediction module to the monitoring module(which is optional).

In this example, the prediction module then checks accuracy of themachine learning model. In case the accuracy is below a predeterminedthreshold accuracy, the prediction module sends an accuracy warningreport to the training module. Based on the accuracy warning report, thetraining module sends an activation measurement request on all beams tothe measurement collector. The process may then be continued as outlinedabove with regard to step S208 in FIG. 2.

In the sequence chart of FIG. 4, the monitoring module is illustrated asbeing optional, as outlined above.

As shown in FIG. 4, a case is illustrated when there has not been amachine learning model trained before, so that a new machine learningmodel is created (step 8 in FIG. 4), and an accuracy warning reportneeds to be generated by the prediction module (step 23 in FIG. 4), ifnecessary. The further process from step 23 onwards is the repetition ofthe previous mechanisms as depicted in FIGS. 2 and 3.

It is to be noted that, as already described above, in FIG. 1 it isassumed that there is one model per 5G site, i.e. one machine learningmodel is calculated for the set of beams belonging to one site. However,other aggregation of the measurements may be applied. For instance, themachine learning model may be calculated for those beam IDs which aremost often seen as, for example, top 10 (or another number of) beam IDsby the reporting user terminal or one model per beam in the extremecase.

FIG. 5 illustrates a graphical user interface (GUI) corresponding to abeam selection algorithm generally as described herein.

In this example, the 5G radio has 48 beams (beams 502, 508 and 510combined), as illustrated in FIG. 5. The dot 504 represents the trackeduser equipment for which the prediction is executed. The beams 508 are,in this example, the six reference beams (i.e. the beams which are, inthis example, always switched on and measured) and the other beams 502out of the 48 beams are the ones that are predicted by the model (theprediction module may select the reference beams 508 in some examples aswell). The beam 510 is the actually selected serving beam for the userequipment, according to the prediction.

In this example implementation, the training module is fed bymeasurements from multiple user equipment 506 and 504 connected to thesame 5G radio board. The machine learning model can therefore begenerated in a shorter amount of time. All multiple user equipmentmeasure the channel quality on the reference beams, which is fed to themachine learning training process. Although the prediction is executedonly for the user equipment 504 in this example, multiple user equipmentmay be tracked similarly using the same model and algorithm. In theimplementation of this example, a Random Forest ensemble learningalgorithm for classification from the set of supervised learningalgorithms is used. Thus, the machine learning model is a decision treecombined from several other decision trees in this case.

FIG. 6 illustrates delay monitoring charts including machine learningtraining and prediction delay.

The delay of one machine learning model computation is illustrated. Itcan be seen that the delay values (upper chart of FIG. 6) are around 600msec to 1 second in this example. For illustration purposes, the machinelearning model is continuously recalculated regardless of its accuracyin this example.

The lower chart in FIG. 6 shows the delay of the prediction executed inthe 5G radio unit. It is about 1.4 msec in average, which means that itis below the 5-10 msec requirement, even if the implementation of theprediction is not optimized for performance in this example.

FIG. 7 illustrates accuracy monitoring charts of the machine learningmodel.

The upper chart in FIG. 7 shows that at about 95% of the time theprocess is being performed, the proposed algorithm selects the true bestbeam (i.e. the one with the highest channel quality) for the trackeduser. In the middle of the charts, and around 11:30:10, the machinelearning model training has been restarted in this scenario which causeddegradation of accuracy. The accuracy is illustrated in the lower chartas well, where the best channel quality (best Reference Signal ReceiverPower, RSRP) in dBm is compared to the channel quality of the beamselected by the algorithm (predicted RSRP). The closer the lines are,the more accurate the machine learning model is.

In order to realize the above and further functionalities regarding theselection of an antenna beam in a 5G radio access network, an antennabeam selection apparatus 802 is provided in embodiments, as shown inFIG. 8.

The antenna beam selection apparatus 802 comprises a measurement module102, a training module 104, a prediction module 106 and a monitoringmodule 110. The modules 102, 104, 106 and 110 may be configured ashardware entities or may be stored as computer program code in one ormore memories. As outlined above, in some examples, the training module104 and the monitoring module 110 may be comprised in a cloudenvironment.

The antenna beam selection apparatus 802 is further shown in FIG. 9, inwhich the antenna beam selection apparatus 802 comprises a 5G radio unit114 and a cloud environment 116. The 5G radio unit 114 comprises aprocessor 904 and a memory 906. The memory 906 is coupled to theprocessor 904 and comprises program code portions that allowcommunication from the 5G radio unit 114 to the user equipment 112,obtaining and collecting channel quality measurements, configuringmeasurements performed by the 5G baseband processing unit 108,forwarding channel quality measurements to the training module 104,obtaining and analyzing channel quality on reference beams, triggering(in)activation of channel quality measurements, predicting the best(highest-quality) beam and switching the 5G baseband processing unit 108to communicate with the user equipment 112 on the predicted best beam.In embodiments in which a monitoring module 110 is provided, the programcode portions further allow performance reports to be sent from theprediction module 106 to the monitoring module 110.

This cloud environment 116 comprises a processor 910 and a memory 912.The memory 912 is coupled to the processor 910 and comprises programcode portions that allow processing channel quality measurementsreceived from the 5G radio unit 114, generating a machine learning modeland providing the machine learning model to the 5G radio unit 114. Inembodiments in which a monitoring module 110 is provided, the programcode portions further allow performance reports to be sent by thetraining module 104 to the monitoring module 110.

Generally, the program code portions comprised in the memories 906 and912 may allow for any of the processes as described above, in particularwith regard to FIGS. 1 to 7, to be performed.

It is to be noted that in embodiments in which the 5G radio unit 114 andthe training module 104 (and the optional monitoring module 110) are notphysically separated, i.e. in particular in which the training module104 (and the optional monitoring module 110) is not comprised in acloud, the processors 904 and 910 may be integral to a single processorand/or the memories 906 and 912 may be integral to a single memory.

FIG. 10 shows a flowchart 1000 of a method for antenna beam selection ina 5G radio access network according to variant as described herein.

At step 1002, channel quality measurements of a plurality of beamsusable to serve a user equipment in the 5G radio access network areobtained. At step 1004, the machine learning model is generated based onthe channel quality measurements. At step 1006, one of the plurality ofbeams is selected, based on the machine learning model, to serve theuser equipment. In some examples, the generation of the machine learningmodel is performed in a cloud environment.

As described above, in FIG. 1 it is assumed that there is one model per5G site, i.e. one machine learning model is calculated for the set ofbeams belonging to one site. However, other aggregation of themeasurements may be applied. For instance, the machine learning modelmay be calculated for those beam IDs which are most often seen as, forexample, top 10 (or another number of) beam IDs by the reporting userterminal or one model per beam in the extreme case.

FIG. 11 illustrates an example with multiple cell sites, each having anumber of beams serving that site. In the figure, different sets ofcells are indicated with different texture types that belong todifferent ML models. This means that one set of cells with texture type1is used to create one model (recall that the model creates therelationship between the reference beams in the set and the best beam inthat set of cells) and another set of cells with a different texturetype constitutes the input parameters of another model. Then, dependingin which set of cells the user is currently located, the correspondingmodel is used to predict the best beam to serve that user.

It is to be noted that one cell may belong to multiple models, so thatoverlaps between the models are possible (and in some instancesnecessary) to achieve good or improved overall prediction accuracy.

It is a further sub-method of the proposed solution of how to createthese sets of beams that constitute one model. Since there can be manydifferent solutions, i.e., one extreme when there is only one model andall beams belong to that model, it may depend on the weighting ofdifferent advantages and disadvantages of a model size in the particularcase.

One way could be to use a clustering algorithm (a special category ofmachine learning algorithms) to determine quasi optimal clusters ofcells that constitute one ML model each. In order to achieve this, themeasurement samples collected during the measurement phase of theprocedure can be used and a metric of distance between the differentmeasurement samples may be defined. Note that a measurement sample isthe vector of a measured beam signal strength measured and reported byone particular user terminal. The distance could be the number ofdifferent beam IDs with non-zero measurement value in the measurementvector. This means that the more the number of different beams ismeasured by two samples, the larger the distance between the two vectorsand the more likely to separate them into different clusters. It is tobe noted that there can be other algorithms than clustering methods usedto create ML model sets.

As will be apparent from the above, embodiments of the systems andmethods as described herein allow for significantly reducing therequired channel quality measurements performed by the terminal. Anear-optimal beam selection algorithm is presented that uses machinelearning techniques to minimize as far as possible the number of channelquality measurements required to select the best beam for the userequipment.

It has been shown that embodiments of the systems and methods describedherein may be applicable in practical systems, as shown for example inthe results displayed in FIGS. 5 to 7 in which the systems and methodsare implemented and evaluated in a real 5G radio testbed.

Embodiments of the systems and methods as described herein thereforeenable achieving energy savings (by activating predicted beams only).

Embodiments of the systems and methods as described herein furtherprovide for a fast prediction and execution while negligible computationoverhead is put on the custom hardware of the radio unit and the cloudcompute power is utilized as much as possible (which requires no extrahardware or compute on the 5G radio board).

Embodiments of the system as described herein fit well into distributedanalytics architectures proposed in the art which may therefore be acandidate for being part of the 5G radio access network architecture.

No doubt many other effective alternatives will occur to the skilledperson. It will be understood that the present disclosure is not limitedto the described variants and encompasses modifications apparent tothose skilled in the art and lying within the scope of the claimsappended hereto.

The invention claimed is:
 1. A system for antenna beam selection in aradio access network, the system comprising: a measurement moduleconfigured to obtain channel quality measurements of a plurality ofbeams usable to serve a user equipment in the radio access network; atraining module coupled to the measurement module, wherein the trainingmodule is configured to generate a machine learning model based on thechannel quality measurements; and a prediction module coupled to thetraining module, wherein the prediction module is configured to: receivethe machine learning model from the training module, and select, basedon the machine learning model, one of the plurality of beams used toserve the user equipment; wherein the prediction module is furtherconfigured to: continuously determine whether a new machine learningmodel is received from the training module, and when the channel qualitymeasurements are obtained by the measurement module: send a request tothe measurement module to provide, to the prediction module, channelquality of a subset of the plurality of beams, predict the selected beambased on the channel quality of the subset of the plurality of beams byfeeding the channel quality measurements into the machine learningmodel, and output the selected beam to cause a baseband processing unitto switch its connection with the user equipment to the selected beam.2. The system of claim 1, wherein the prediction module is furtherconfigured to perform the sending of the request to the measurementmodule to provide, to the prediction module, channel quality of thesubset of the plurality of beams, the predicting of the selected beambased on the channel quality of the subset of the plurality of beams andthe outputting of the selected beam to cause the baseband processingunit to switch its connection with the user equipment to the selectedbeam until a time period, starting from the new machine learning modelbeing received by the prediction module, exceeds a predeterminedthreshold time period.
 3. The system of claim 1, wherein the predictionmodule is further configured to: periodically determine accuracy of themachine learning model received from the training module, and if theaccuracy is larger than a threshold accuracy, send a measurementinactivation command to the measurement module to inactivate channelquality measurements to be obtained for beams not comprised in thesubset of the plurality of beams, and if the accuracy is smaller thanthe threshold accuracy, send an accuracy warning report to the trainingmodule to cause the training module to refine the machine learningmodel.
 4. The system of claim 3, wherein the prediction module isfurther configured to perform the determination of accuracy of themachine learning model by: measuring the channel quality of each of theplurality of beams, and comparing the outcome of the measurement of thechannel quality of each of the plurality of beams with the machinelearning model received from the training module.
 5. The system of claim1, wherein the system is configured to select the subset from theplurality of beams such that the beams of the subset cover asubstantially uniform area of the plurality of beams.
 6. The system ofclaim 1, wherein, during a prediction phase, the prediction of theselected beam by the prediction module is based only on the channelquality of the subset of the plurality of beams.
 7. The system of claim6, wherein the measurement module is configured to obtain and provide tothe training module, during a training phase, all channel qualitymeasurements of the plurality of beams, and wherein the training moduleis configured to update the machine learning model based on all channelquality measurements of the plurality of beams received from themeasurement module, and further wherein the system is configured toswitch between the training phase and the prediction phase.
 8. Thesystem of claim 7, wherein the system is further configured to generatefrom a multiple switching between the training phase and the predictionphase a heuristic model usable to predict the beam with the highestchannel quality among the plurality of beams.
 9. A method for antennabeam selection in a radio access network, the method comprising:obtaining channel quality measurements of a plurality of beams useableto serve a user equipment in the radio access network; generating amachine learning model based on the channel quality measurements; andselecting, based on the machine learning model, one of the plurality ofbeams to serve the user equipment; wherein selecting one of theplurality of beams to serve the user equipment comprises predicting theselected beam based on a subset of the plurality of beams by feeding thechannel quality measurements into the machine learning model, andoutputting an indication of the selected beam, to cause a basebandprocessing unit of the radio access network to switch its connectionwith the user equipment to the selected beam.
 10. The method of claim 9,further comprising periodically determining an accuracy of the machinelearning model and, if the accuracy is below a threshold accuracy,initiating a refinement of the machine learning model, and, if theaccuracy is above the threshold accuracy, inactivating channel qualitymeasurements for beams not in the subset of the plurality of beams. 11.The method of claim 10, further comprising determining the accuracy ofthe machine learning model by: measuring the channel quality of each ofthe plurality of beams, and comparing the outcome of the measurement ofthe channel quality of each of the plurality of beams with the machinelearning model.
 12. The method of claim 9, further comprising selectingthe subset of beams from the plurality of beams, such that the beams ofthe subset cover a substantially uniform area of the plurality of beams.13. The method of claim 9, wherein, during a prediction phase, theprediction of the selected beam by the prediction module is based onlyon the channel quality of the subset of the plurality of beams.
 14. Amethod of beam selection for a radio access network, the methodcomprising: collecting channel quality measurements for a plurality ofbeams that are usable by the radio access network for serving a UserEquipment (UE), the plurality of beams including a subset of referencebeams that are uniformly spaced within an overall coverage areacorresponding to the plurality of beams; using the collected channelquality measurements to determine channel-quality relationships betweenbeams in the subset and remaining beams in the plurality of beams;predicting channel qualities for the remaining beams in the plurality ofbeams, using updated channel quality measurements for the subset ofreference beams and the determined channel-quality relationships; andselect one of the beams in the plurality of beams, in dependence on thepredicted channel qualities.